The model we consider is similar to the work of [1]} in their SD-GANs, but applied to StyleGAN2. We begin with a generator \(G\)  that takes two input latent codes, \(z_1 \in \mathbb {R}^{512}\)  and \(z_2 \in \mathbb {R}^{512}\) , both drawn from a standard normal distribution. The codes \(z_1\)  and \(z_2\)  are intended to correspond to identity and non-identity features respectively. Both input codes are then passed through separate fully connected layers which are trained independently. They are then concatenated to form a layer of size \(\mathbb {R}^{1024}\)  which is the reduced to an embedding layer \(\in \mathbb {R}^{512}\)  from another fully connected layer. A schematic of the architecture is shown in Figure REF .
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